1.3k citations · 3.7k across the 23 of their papers we have counts for
23 papers
FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition
Linshan Wu, Jiaxin Zhuang, Yanning Zhou +12
Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilit…
A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis
Wenhui Lei, Hanyu Chen, Zitian Zhang +13
AI-assisted imaging made substantial advances in tumor diagnosis and management. However, a major barrier to developing robust oncology foundation models is the scarcity of large-s…
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
Zifeng Wang, Hanyin Wang, Benjamin Danek +6
The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to cli…
ReXplain: Translating Radiology into Patient-Friendly Video Reports
Luyang Luo, Jenanan Vairavamurthy, Xiaoman Zhang +7
Radiology reports, designed for efficient communication between medical experts, often remain incomprehensible to patients. This inaccessibility could potentially lead to anxiety,…
ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics
Oishi Banerjee, Agustina Saenz, Kay Wu +17
Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology…
A Large Model for Non-invasive and Personalized Management of Breast Cancer from Multiparametric MRI
Luyang Luo, Mingxiang Wu, Mei Li +8
Breast Magnetic Resonance Imaging (MRI) demonstrates the highest sensitivity for breast cancer detection among imaging modalities and is standard practice for high-risk women. Inte…
Learning Generalized Medical Image Representations through Image-Graph Contrastive Pretraining
Sameer Khanna, Daniel Michael, Marinka Zitnik +1
Medical image interpretation using deep learning has shown promise but often requires extensive expert-annotated datasets. To reduce this annotation burden, we develop an Image-Gra…
FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores
Alyssa Huang, Oishi Banerjee, Kay Wu +2
The current gold standard for evaluating generated chest x-ray (CXR) reports is through radiologist annotations. However, this process can be extremely time-consuming and costly, e…
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Sunan He, Yuxiang Nie, Hongmei Wang +21
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of m…
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
Emma Chen, Aman Kansal, Julie Chen +4
We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a comprehensive benchmark for evaluating foundation models in Emergency Medicine using a dataset of 100K+…
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
Benjamin Yan, Ruochen Liu, David E. Kuo +8
Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generati…
Exploring the Boundaries of GPT-4 in Radiology
Qianchu Liu, Stephanie Hyland, Shruthi Bannur +16
The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domai…
Deep Learning for the Digital Pathologic Diagnosis of Cholangiocarcinoma and Hepatocellular Carcinoma: Evaluating the Impact of a Web-based Diagnostic Assistant
Bora Uyumazturk, Amirhossein Kiani, Pranav Rajpurkar +17
While artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, the question of how best to incorporate these algorithms into clin…
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko +17
Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that conta…
Know What You Don't Know: Unanswerable Questions for SQuAD
Pranav Rajpurkar, Robin Jia, Percy Liang
Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for whi…
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu +9
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural networ…
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
Pranav Rajpurkar, Jeremy Irvin, Aarti Bagul +11
We introduce MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either norma…
Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning
Pranav Rajpurkar, Vinaya Polamreddi, Anusha Balakrishnan
We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL ag…
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi +2
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single…
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev +1
We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia article…
Augur: Mining Human Behaviors from Fiction to Power Interactive Systems
Ethan Fast, William McGrath, Pranav Rajpurkar +1
From smart homes that prepare coffee when we wake, to phones that know not to interrupt us during important conversations, our collective visions of HCI imagine a future in which c…
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
Pranav Rajpurkar, Toki Migimatsu, Jeff Kiske +4
While emerging deep-learning systems have outclassed knowledge-based approaches in many tasks, their application to detection tasks for autonomous technologies remains an open fiel…
An Empirical Evaluation of Deep Learning on Highway Driving
Brody Huval, Tao Wang, Sameep Tandon +10
Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical ev…